Public talk on Spatial Analysis and Estimation of Petrophysical Properties
21 April 2016 - A public lecture on “Spatial Analysis and Estimation of Petrophysical Properties by Professor Steve Tyson was held today at the Lecture Theatre 2, Universiti Teknologi Brunei. The public lecture was organised by the Faculty of Engineering, UTB.
Professor Steve Tyson is from University of Queensland, Australia has more than 30 years industry experience before moving to academia in 2013. He has worked in a range of subsurface roles, specialising in modelling, gridding, upscaling and spatial statistics. During this time he has developed a number of algorithms to improve model quality and quantify model uncertainty. Since 2013 Prof Steve has been the Chair of Subsurface Modelling in the Centre for Coal Seam Gas and the Director of the Centre for Geoscience Computing in the School of Earth Sciences, both at The University of Queensland. He oversees more than $3.5M of research projects in the geosciences covering geophysics, geostatistics, geology, uncertainty modelling and data analytics.
In his talk he discussed the existing geostatistics algorithms based on the kriging matrix that is shown to underestimate the connectivity of extreme values because they assume a linear spatial dependence model. Moreover, the estimation of uncertainty based on these techniques uses the kriging variance, which is not dependent on the values of the spatially distributed variable. It was also added that these uncertainty estimate are often implausible.
He also explained the reasons why most spatial variables in geoscience do not have a linear spatial dependence, even after monotonic transformations, and the impact of this in the estimation of petrophysical properties.
More recent techniques such as multiple-point statistics and non-linear geostatistics have been developed to overcome these limitations and each of these techniques has advantages and disadvantages were discussed. The talk offered the opportunity to consider new concepts in geostatistical thinking and determine their applicability to the petroleum industry.
